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A quantitative comparison of 3D face databases for 3D face recognition

机译：用于3D人脸识别的3D人脸数据库的定量比较

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摘要

During the last decade research in face recognition has shifted from 2D to 3D face representations. The need for 3D face data has resulted in the advent of 3D databases. In this paper, we first give an overview of publicly available 3D face databases containing expression variations, since these variations are an important challenge in today's research. The existence of many databases demands a quantitative comparison of these databases in order to compare more objectively the performances of the various methods available in literature. The ICP algorithm is used as baseline algorithm for this quantitative comparison for the identification and verification scenario, allowing to order the databases according to their inherent difficulty. Performance analysis using the rank 1 recognition rate for identification and the equal error rate for verification reveals that the FRGC v2 database can be considered as the most challenging. Therefore, we recommend to use this database further as reference database to evaluate (expression-invariant) 3D face recognition algorithms. As second contribution, the main factors that influence the performance of the baseline technique are determined and attempted to be quantified. It appears that (1) pose variations away from frontality degrade performance, (2) expression types affect results, (3) more intense expressions degrade recognition, (4) an increasing number of expressions decreases performance and (5) the number of gallery subjects degrades performace. A new 3D face recognition algorithm should be evaluated for all these factors.
机译：在过去的十年中，人脸识别研究已从2D转变为3D人脸表示。对3D人脸数据的需求导致3D数据库的出现。在本文中，我们首先概述了包含表情变化的可公开使用的3D人脸数据库，因为这些变化是当今研究的重要挑战。许多数据库的存在要求对这些数据库进行定量比较，以便更客观地比较文献中各种方法的性能。 ICP算法用作基线算法，用于鉴定和验证场景的定量比较，从而可以根据数据库的固有难度对数据库进行排序。使用1级识别率进行识别和相等错误率进行验证的性能分析表明，FRGC v2数据库可以认为是最具挑战性的数据库。因此，我们建议将该数据库进一步用作参考数据库，以评估（表达式不变）3D人脸识别算法。作为第二贡献，确定并尝试量化影响基线技术性能的主要因素。看起来（1）姿势变化远离正面性会降低性能；（2）表达式类型会影响结果；（3）较激烈的表达式会降低识别度；（4）表达式数量增加会降低性能；（5）画廊主题的数量降低性能。应该针对所有这些因素评估一种新的3D人脸识别算法。

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